Bayesian Markov Chain Monte Carlo for reparameterized Stochastic volatility models using Asian FX rates during Covid-19

In this paper, reparameterization and student-t are applied to Stochastic Volatility (SV) model. We aim to reduce the amount of autocorrelation of the SV parameters and to introduce heavy-tailed model via the Bayesian computation of the Markov Chain Monte Carlo (MCMC) samplers. This research paper h...

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Detalles Bibliográficos
Autor principal: Poonvoralak, Wantanee
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2022
Materias:
Application Notes
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10228328/
https://www.ncbi.nlm.nih.gov/pubmed/37260472
http://dx.doi.org/10.1080/02664763.2022.2064440
Descripción
Sumario:In this paper, reparameterization and student-t are applied to Stochastic Volatility (SV) model. We aim to reduce the amount of autocorrelation of the SV parameters and to introduce heavy-tailed model via the Bayesian computation of the Markov Chain Monte Carlo (MCMC) samplers. This research paper helps support better MCMC estimation of the SV model for volatile Asian FX series during Covid-19.

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